Centre National de la Recherche Scientifique, Paris, France.
Comput Med Imaging Graph. 2011 Oct-Dec;35(7-8):579-91. doi: 10.1016/j.compmedimag.2010.11.009. Epub 2010 Dec 10.
Histopathological examination is a powerful standard for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of whole slide images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on application-driven for high-resolution and generic for low-resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. Sparse coding and dynamic sampling constitute the keystone of our approach. These methods are implemented within a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40×) high power fields. The processing time to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology.
组织病理学检查是对危急疾病进行预后判断的有力标准。然而，尽管高速、高分辨率扫描设备或虚拟探测能力取得了显著进步，全切片图像（WSI）的临床分析仍然主要依赖于人类专家的工作。我们提出了一个创新的平台，其中多尺度计算机视觉算法对组织病理学 WSI 进行快速分析。该平台依赖于应用驱动的高分辨率分析算法和通用的低分辨率分析算法，这些算法嵌入在一个多尺度框架中，以便快速识别病理学家用于评估整体分级的高放大倍率感兴趣区域。GPU 技术也提高了系统的全局效率。稀疏编码和动态采样是我们方法的基石。这些方法是在一个基于组织病理学图像的计算机辅助乳腺活检分析应用程序中实现的，该应用程序是与病理科合作设计的。目前的真实幻灯片对应大约 36000 个高倍放大（40×）高倍视野。实现自动 WSI 分析的处理时间与病理学家的表现相当（大约十分钟一个 WSI），这本身就是所提出方法的一个主要贡献。
